Predicting Brain Stroke Using Machine Learning

Abstract

On the basis of the GBD (Global Burden of Disease) 2013 Study, this article provides an overview of the global, regional, and country-specific burden of stroke by sex and age groups, including trends in stroke burden from 1990 to 2013, and outlines recommended measures to reduce stroke burden [1]. The mind functions as the primary upper body organ for humans. A stroke is a medical condition wherein the blood vessels in the brain burst, resulting in brain damage. The interruption of blood and nutrient supply to the brain might cause symptoms. It is considered a medical emergency and might result in long-term neurological damage, complications, and sometimes death. According to the World Health Organization, stroke is the leading cause of death and disability globally. Early recognition of symptoms and seeking medical attention will reduce the disease’s severity. This paper uses deep learning and machine learning techniques to predict the possibility of a brain stroke occurring early-on. A reliable dataset for stroke prediction was acquired from Kaggle to test the effectiveness of the algorithm. The Random Forest classifier achieved the highest classification accuracy of 97% among the machine learning classifiers.